A data-driven solution for abandoned object detection: Advantages of multiple types of diversity

The automated detection of abandoned objects is a quickly developing and widely researched field in video processing with specific application to automated surveillance. In the recent years, a number of approaches have been proposed to automatically detect abandoned objects. However, these techniques require prior knowledge of certain properties of the object such as its shape and color, to classify the foreground objects as abandoned object. The performance of tracking-based approaches degrades in complex scenes, i.e., when the abandoned object is occluded or in the case of crowding. In this paper, we propose a data-driven approach based on independent component analysis (ICA) for object detection. We demonstrate the success of the proposed ICA-based methodology with examples of videos with complex scenarios. We also show that algorithm choice plays an important role in performance, in particular when multiple types of diversities are taken into account and demonstrate the importance of order selection.